Power generation portfolios: A parametric formulation of the efficient frontier

Juárez-Luna, David (2019): Power generation portfolios: A parametric formulation of the efficient frontier.

Abstract

The Portfolio Theory has been extensively used as a planning tool for power generation diversification. However, no one of the existing papers provide a detailed explanation on how the efficient frontier of the Power Generation Portfolio (PGP) is costructed. We provide a parametric formulation of the efficient frontier of PGP of up to 5 technologies. The analysys takes advantages of the fact that the risk of the PGP is a convex function of the shares of the different technologies. The parametric formulation of the efficient frontier of the PGP constitutes a powerfull policy tool for power generation policy-makers.

• Item Type: MPRA Paper
• Language: English
• Keywords: Portfolio, Power Generation, Efficient Frontier, Risk, NPV.
• Subjects: D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D81 - Criteria for Decision-Making under Risk and Uncertainty; G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q40 - General; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q49 - Other

References

Adams, R., Jamasb, T., 2016. Optimal Power Generation Portfolios: An Application to the UK, Cambridge Working Papers in Economics 1646 / Energy Policy Research Group (EPRG), Working Paper 1620, August, Faculty of Economics, University of Cambridge.

Awerbuch, S., Berger, M., 2003. Applying portfolio Theory to EU Electricity Planning and Policy-Making. IEA/EET Working Paper. IEA. Paris

Costa, O. L.V., Ribeiro, C. O., Rego, E. E., Stern, J. M., Parente, V., Kileber, S., 2017. Robust portfolio optimization for electricity planning: An application based on the Brazilian electricity mix. Energy Economics 64: 158--169.

Cunha, J., Ferreira, P., 2014. Designing electricity generation portfolios using the mean-variance approach, Int. J. Sustain. Energy Plan. Manag., 4: 17--30.

DeLlano-Paz, F., Calvo-Silvosa, A., Antelo, S. I., Soares, I., 2017. Energy planning and modern portfolio theory: A review. Renewable and Sustainable Energy Review 77: 636--651.

Freund, J. E., Miller, I, Miller, M., 2000. Estadística matemática con aplicaciones. Pearson educación.

Jain, S., Roelofs, F., Oosterlee, C. W., 2014. Decision-support tool for assessing future nuclear reactor generation portfolios. Energy Economics 44: 99-112.

Jansen, J.C., Beurskens, L.W.M., Tilburg, X.V., 2006. Application of portfolio analysis to the Dutch generating mix. ECN report C-05-100. Energy research council of Netherlands.

Markowitz H. M., 1952. Portfolio Selection, Journal of Finance, Vol. 7, pp 77-91.

Pinheiro Neto, D., Domingues, E. G., Coimbra, A. P., de Almeida, A. T., Alves, A. J., Calixto W. P., (2017), "Portfolio optimization of renewable energy assets: hydro, wind, and photovoltaic energy in the regulated market in Brazil," Energy Economics, vol. 64, pp. 238--250, 2017.

Roques, F. A., Hiroux, C., Saguan, M., 2010. Optimal wind power deployment in Europe --- A portfolio approach, Energy Policy 38, pp. 3245-3256.

Roques, F. A., Newbery, D. M., Nuttall, W. J., 2008. Fuel mix diversification incentives in liberalized electricity markets: A Mean--Variance Portfolio theory approach, Energy Economics, Volume 30, Issue 4: 1831-1849.

Vithayasrichareon, P., MacGill, I.F., Wen, F.S., 2010a. Electricity generation portfolio evaluation for highly uncertain and carbon constrained future electricity industries. IEEE Power and Energy Society General Meeting.

Vithayasrichareon, P., MacGill, I.F., Wen, F., 2010b. Electricity Generation Portfolio Analysis for Coal, Gas and Nuclear Plant under Future Uncertainties. 4th IASTED Asian Conference on Power and Energy Systems